This disclosure relates to tracking of wireless devices, and more particularly, to tracking of wireless devices using a single-point signal monitor.
The popular, almost addictive, usage of wireless devices and their data-intensive apps create novel opportunities to exploit their network traffic for monitoring and optimizing real-world processes. For example, cellular call data records can be used to infer large scale transportation patterns, or cellular signal traces may allow inferring the level of congestion on roadways.
A familiar and often frustrating occurrence is waiting in service queues in retail stores, banks, theme parks, hospitals and transportation stations. A service queue in these environments may include the waiting period, the service period and the leaving period. As people arrive, the waiting period is the time spent waiting for the service. During the service period people receive service, such as paying for items in a store or checking-in travel bags at the airport. A person exits the service area during the leaving period. Note that the concept of a service queue can be interpreted loosely, people do not need to stand in line but could sit in a waiting room and do not always need to be served in a strict first in, first out order.
Real-time quantification of the waiting and service times in such service queues allows optimizing service processes, ranging from retail, to heath care, to transportation and entertainment. For example, many hospital emergency department surveys have average waiting times of several hours. More complete waiting and service time statistics allow customers, travelers, managers and service providers to make changes to their staffing and/or procedural processes. For example, an airport checkpoint might be experiencing abnormal delays and require interventions by diverting or relocating screeners from queues with shorter waiting times. Customers also can benefit, for example, knowing at what times retail store checkout lines can be expected to be shorter, a customer can decide whether to stay in the queue or go to do more urgent tasks. Managers can use such information to make staffing decisions based on the service length. For example, during particular hours in a day, service times may grow at a coffee shop due to increased demands for espresso drinks compared to other items. In such a case, it might be more effective to change the staffing to use experienced baristas as opposed to simply adding staff. A hospital emergency department may shift nursing staff to assist with triage when waiting times become too long. In the transportation field, bus and train schedules or boarding and payment processes could be adjusted.
Existing solutions to the queue monitoring problem rely on cameras, such as infrared or special sensors, such as floor mats, and usually require sensors at multiple locations. These techniques using wireless networks were too coarse-grained to differentiate between the waiting and service time. Moreover, these solutions require multiple sensors to fully monitor a single longer queue, which increases installation and system cost. Further, earlier camera solutions are prone to occlusion and may require multiple cameras (networked together) to provide a complete measurement. Multi-camera setups are more costly in hardware and installation and may encounter privacy concerns because customers' facial identities are captured by the cameras.
This document describes devices and methods that are intended to address issues discussed above and/or other issues.